Time Series for Forecasting Stock Market Prices Based on Sentiment Analysis of Social Media

2. Literature Survey

Venkata et al.(2016) used Word2vec and N-gram representation of text to train a classifier model to predict the stock market movements and picked Word2vec representation due to its high accuracy in large datasets. Rakhi et al.(2018) collected the sentiment data, and the stock price data to predict stock market price using a support-vector machine (SVM) classifier and observed that if the data size increases the accuracy obtained will also increase. Scott et al.(2017) used smart user classification to filter the tweets by computing scoring weights based on number of likes, number of followers count and how often the user is correct. Further, they used Tf-Idf vectorizer for textual representation and linear regression classifier for the sentiment prediction. Zhaoxia et al.(n.d.) used the sentiments of the news data to predict the stock market price using neural networks.

Sreelekshmy et. al.(2017) applied Recurrent Neural Networks(RNN), Long short-term memory(LSTM) and Convolutional Neural Networks(CNN) - sliding window architecture for stock price prediction of Infosys, TCS and Cipla and concluded that CNN outperforms the other two models in the stock market analysis due to the irregular changes that happen in the stock market. Few works have used the previous stock market data to predict the movements of the stock market while another few used the sentiments from social media to predict the same using SVM, random forest and other machine learning algorithms. Also it is clear that Word2Vec representation of text will be ideal for data that is fed into the neural network layers for building the classifier that predicts the trends of the stock market.